[Purpose/Significance] The current practices of data classification and grading vary greatly across regions in China. It is important to clarify the current state of system construction and explore optimization paths for the modernization of data governance capabilities. [Method/Process] Using 114 local policy documents related to data classification and grading in China as research samples, this study employed procedural grounded theory to derive 138 initial concepts, 12 basic categories, and 5 core categories. A model of China’s data classification and grading system was constructed from five dimensions: field, management, criteria, objectives, and guarantees. [Result/Conclusion] The study reveals deficiencies in areas such as multi-stakeholder collaboration, management refinement, basis optimization, monitoring balance, and objective planning in China’s data classification and grading system. Based on this, the study proposes the following optimization paths, including continuing the “government-led + multi-party participation” collaborative model; enhancing the scientific and practical aspects of management mechanisms; improving the foresight and comprehensiveness of the criteria design; addressing monitoring weaknesses by adopting a “strict as standard” approach; and coordinating strategic and functional objectives.
Zhang Junrong
,
Qiao Jianzhi
. Research on the Optimization Path of China’s Data Classification and Grading System Based on Grounded Theory[J]. Library and Information Service, 2026
, 70(1)
: 82
-93
.
DOI: 10.13266/j.issn.0252-3116.2026.01.007
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